Lead Machine Learning Engineer

Harnham; Toronto; Hybrid; CAD 140,000 - 160,000; Full time

Job summary

A leading organization in artificial intelligence is seeking a Senior Machine Learning Engineer in Toronto. You will focus on architecting, building, and deploying highly impactful ML systems, collaborating with data scientists and engineers on state-of-the-art projects. This position offers a competitive salary, including a 15% bonus and generous benefits.

Benefits

15% Annual Bonus

Generous benefits package including health, dental, and vision insurance

Professional development budget

Opportunities to attend AI conferences

Qualifications

• Experience with LLMs and transformer-based architectures.
• Proven experience in deploying ML models for production.
• Familiarity with cloud platforms (AWS preferred).

Responsibilities

• Lead development of ML/LLM solutions for tasks such as classification and summarization.
• Fine-tune and optimize pre-trained LLMs using industry best practices.
• Collaborate cross-functionally to apply advanced AI to real-world problems.

Skills

Python programming

Strong communication skills

ML tools experience

Education

MSc or PhD in Computer Science, Machine Learning, Engineering, or related STEM field

Tools

MLFlow

Airflow

TensorFlow

PyTorch

Docker

Kubernetes

Job description

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This range is provided by Harnham. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$140,000.00/yr - $160,000.00/yr

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Building AI/ML & Data/Software Engineering Teams Across The US

Location: Toronto

Salary: $140,000 – $160,000 base + 15% Bonus

Company Overview:

Join a fast-growing, innovative organization at the forefront of artificial intelligence, committed to pushing boundaries in generative AI and Large Language Models (LLMs). They are solving cutting-edge problems with scalable machine learning and deploying solutions across industries.

Role Overview:

As a Senior Machine Learning Engineer with a specialization in LLMs, you will play a pivotal role in architecting, building, and deploying high-impact machine learning systems. You’ll collaborate cross-functionally with data scientists, research engineers, and business stakeholders to drive real-world applications of advanced AI.

Key Responsibilities:

• Lead development of ML/LLM solutions for tasks like summarization, classification, Q&A, and RAG.
• Collaborate on transformer models (e.g., GPT, LLaMA, Claude, Mistral).
• Fine-tune and optimize pre-trained LLMs using best practices.
• Build and maintain ML pipelines with MLFlow, Airflow, or Kubeflow.
• Partner with MLOps/DevOps to ensure scalable, secure production systems.
• Deploy models using Docker, Kubernetes, and serving frameworks (e.g., TensorFlow Serving, TorchServe, FastAPI).
• Implement model versioning, blue-green/canary deployments, and performance monitoring.
• Develop scalable data pipelines for text and embeddings.
• Stay up to date with LLM/AI research and apply findings to real-world problems.
• Document workflows and support knowledge sharing across the team.
• Lead and mentor a team of ML engineers and researchers to deliver high-impact solutions.

Required Qualifications:

• MSc or PhD in Computer Science, Machine Learning, Engineering, Mathematics, or related STEM field.
• Proven experience with LLMs and transformer-based architectures (e.g., BERT, RoBERTa, GPT, T5).
• Expertise in developing and deploying ML models in production environments.
• Strong Python programming skills; familiarity with ML/AI libraries (Hugging Face Transformers, TensorFlow, PyTorch).
• Experience with cloud platforms (AWS preferred), container orchestration (Kubernetes), and distributed data processing (Apache Spark, Kafka).
• Hands-on experience with ML tools including MLFlow, Airflow, and experiment tracking systems.
• Solid understanding of DevOps and CI/CD pipelines for ML systems.
• Strong communication skills with the ability to articulate technical details to non-technical stakeholders.

Preferred Experience:

• Experience in retrieval-augmented generation (RAG), vector databases (e.g., Pinecone, FAISS, Weaviate), and embedding models.
• Exposure to open-source LLM deployment frameworks like LangChain or LlamaIndex.
• Knowledge of reinforcement learning from human feedback (RLHF), prompt engineering, and evaluation metrics for generative models.
• Prior work in regulated or high-security environments (finance, healthcare, etc.) is a plus.

Compensation and Benefits:

• 15% Annual Bonus
• hybrid work setup
• Generous benefits package including health, dental, and vision insurance
• Professional development budget and opportunities to attend top AI conferences

How to Apply:

To express your interest in this opportunity, please submit your CV via the "Apply" link on this page. We look forward to hearing from you!

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Analyst

• Industries

  Research Services and Software Development

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